Image Matching Method, Image Matching Apparatus, and Program

ABSTRACT

To provide an image matching method, an image matching apparatus, and a program able to perform a matching images at a high accuracy. A Hough transform unit  15  performs a Hough transform processing to a registered image AIM and an image to be matched RIM, in detail, an image processing by which points in each image are transformed to a curved pattern and linear components in each image are transformed to a plurality of the overlapped curved-patterns, based on a distance ρ from a reference position to a shortest point in a straight line L passing through a point in the image and an angle θ between a straight line n 0  passing though the reference position and the shortest point and a x-axis as a reference axis including the reference position, and generating a transformed image S 1621  and a transformed image S 1622 . A judgment unit  164  performs a matching of the registered image AIM and the image to be matched RIM based on a degree of an overlap of the patterns in the transformed image S 1621  and the transformed image S 1622  and a matching or mismatching of the patterns in the same.

TECHNICAL FIELD

The present invention relates to an image matching method and an imagematching apparatus for matching two images such as blood vessel images,fingerprint images, still images or moving images, based on linearcomponents within the images, and a program for the same.

BACKGROUND ART

Conventionally, there is known various image matching apparatuses as anapparatus for performing a matching images to an image information. Forexample, there is known an image matching apparatus in which aregistered image and an image to be matched are compared based on apredetermined positional relationship, correlation values thereof arecalculated, and the matching of the registered image and the image to bematched are carried out based on the correlation values. Otherwise, inthe case where the correlation values are generated, there is also knownan image matching apparatus in which the correlation values aregenerated by an operation in every pixel unit.

DISCLOSURE OF THE INVENTION Problem to be Solved by the Invention

However, in the above image matching apparatus, if, there arepattern-grades such as many linear components in the images even if acorrelation of the two images is small, and then there are many crossedpoints of the linear components in the registered image and the image tobe matched, the crossed points greatly contribute to the correlationvalue to increase the correlation values, and, as a result, a sufficientmatching accuracy may not be obtain. So there is demanded to improve theapparatus.

The present invention was made in consideration of the abovediscussions, and, an object of the present invention is to provide animage matching method, an image matching apparatus, and a program forachieving an image matching at a high accuracy.

Means for Solving the Program

To achieve the above object, according to a first aspect of the presentinvention, there is provided an image matching method for performing amatching images to linear components in a first image and a secondimage, the method having: a first step of performing an image processingfor transforming points in each image of the first image and the secondimage to a curved pattern and transforming the linear component in theimage to a plurality of overlapped curved-patterns, based on a distancefrom a reference position to a shortest point in a straight line passingthrough a point in the image and an angle between a straight linepassing though the reference position and the shortest point and areference axis including the reference position, and generating a firsttransformed image and a second transformed image, and a second step ofperforming a matching of the first image and the second image based on adegree of an overlap of the patterns in the first transformed image andthe second transformed image generated in the first step and a matchingor mismatching of the patterns in the first and second transformedimages.

Further, to achieve the above object, according to a second aspect ofthe present invention, there is provided an image matching method forperforming a matching images to linear components in a first image and asecond image, the method having: a first step of performing a Houghtransform processing to the first image and the second image to generatea first transform image and a second transform image, and a second stepof performing a matching of the first image and the second image basedon a degree of an overlap of patterns in the first transformed image andthe second transformed image generated in the first step and a matchingor mismatching of the patterns in the first and second transformedimages.

Further, to achieve the above object, according to a third aspect of thepresent invention, there is provided an image matching apparatusperforming a matching images to linear components in a first image and asecond image, the apparatus having: a transform means for performing animage processing to the first image and the second image, by whichpoints in each image are transformed to a curved pattern and the linearcomponents in each image are transformed to a plurality of overlappedcurved-patterns based on a distance from a reference position to ashortest point in a straight line passing through a point in the imageand an angle between a straight line passing though the referenceposition and the shortest point and a reference axis including thereference position, and generating a first transformed image and asecond transformed image, and a matching means for performing a matchingof the first image and the second image based on a degree of an overlapof the patterns in the first transformed image and the secondtransformed image generated by the transform means and a matching ormismatching of the patterns in the first and second transformed images.

Further, to achieve the above object, according to a fourth aspect ofthe present invention, there is provided an image matching apparatusperforming a matching images to linear components in a first image and asecond image, the apparatus having: a transform means for performing aHough transform processing to the first image and the second image togenerate a first transformed image and a second transformed image, and amatching means for performing a matching of the first image and thesecond image based on a degree of an overlap of patterns in the firsttransformed image and the second transformed image generated by thetransform means and a matching or mismatching of the patterns in thefirst and second transformed images.

Further, to achieve the above object, according to a fifth aspect of thepresent invention, there is provided a program that causes aninformation processing device to perform a matching images to linearcomponents in a first image and a second image, the program having: afirst routine for performing an image processing to the first image andthe second image, by which points in each image are transformed to acurved pattern and linear components in each image are transformed to aplurality of overlapped curved-patterns based on a distance from areference position to a shortest point through a point in the imagetoward a straight line and an angle between a straight line passingthough the reference position and the shortest point and a referenceaxis including the reference position, and generating a firsttransformed image and a second transformed image, and a second routinefor performing a matching of the first image and the second image basedon a degree of an overlap of the patterns in the first transformed imageand the second transformed image generated in the first routine and amatching or mismatching of the patterns in the same.

Further, to achieve the above object, according to a sixth aspect of thepresent invention, there is provided a program that causes aninformation processing device to perform a matching images to linearcomponents in a first image and a second image, the program having: afirst routine for performing a Hough transform processing to the firstimage and the second image to generate a first transformed image and asecond transformed image, and a second routine for performing a matchingof the first image and the second image based on a degree of an overlapof patterns in the first transformed image and the second transformedimage generated in the first routine and a matching or mismatching ofthe patterns in the first and second transformed images.

According to the present invention, in the first step and in the firstroutine, a step transform means performs an image processing fortransforming points in each image of the first image and the secondimage to a curved pattern and transforming linear components in eachimage to a plurality of overlapped curved-patterns, based on a distancefrom a reference position to a shortest point in a straight line passingthrough a position in the image and an angle between a straight linethrough the reference position and the shortest position and a referenceaxis including the reference position, and generating the firsttransformed image and the second transformed image.

In the second step and in the second routine, the matching means matchesthe first image and the second image, based on the degree of the overlapof the patterns in the first transformed image and the secondtransformed image generated by the transform means and a matching ormismatching of the patterns in the first and second transformed images.

EFFECT OF THE INVENTION

According of the present invention, there is provided an image matchingmethod, an image matching apparatus, and a program, enabling the imagematching at a high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrated in a form of hardwareof an image matching apparatus of a first embodiment according to thepresent invention.

FIG. 2 is a functional block diagram illustrated in a form of softwareof the image matching apparatus shown in FIG. 1.

FIGS. 3A and 3B are views for explaining an operation of a Houghtransform unit shown in FIG. 2.

FIGS. 4A to 4F are views for explaining the operation of the Houghtransform unit shown in FIG. 2.

FIGS. 5A to 5C are views for explaining an operation of a similaritygeneration unit shown in FIG. 2.

FIG. 6 is a flow chart for explaining an operation of the image matchingapparatus according to the present embodiment shown in FIG. 1.

FIG. 7 is a view showing a position correction unit of an image matchingapparatus of a second embodiment according to the present invention.

FIG. 8 is a view for explaining an operation of a Fourier and Mellintransform unit 211 shown in FIG. 7.

FIGS. 9A to 9F are views for explaining different points between aself-correlation method and a phase-only correlation method.

FIGS. 10A to 10C are views for explaining correlation intensitydistributions in the phase-only correlation method in the case where aparallel movement shift exists between two images.

FIGS. 11A to 11C are views for explaining correlation intensitydistributions in the phase-only method in the case where a rotationshift exists between two images.

FIGS. 12A to 12C are views for explaining correlation image data outputby a phase-only correlation unit 23.

FIG. 13 is a view for explaining the correlation image data shown inFIG. 12C.

FIG. 14 is a flow chart for explaining an operation of the imagematching apparatus 1 shown in FIG. 1.

FIG. 15 is a functional block diagram of an image matching apparatus ofa third embodiment according to the present invention.

FIGS. 16A and 16B are views for explaining an operation of a positioncorrection unit shown in FIG. 15.

FIG. 17 is a flow chart for explaining an operation of the imagematching apparatus of the third embodiment according to the presentinvention.

FIG. 18 is a functional block diagram of an image matching apparatus ofa fourth embodiment according to the present invention.

FIGS. 19A to 19F are views for explaining an operation of the imagematching apparatus 1 c shown in FIG. 17; FIG. 19A is a view showing aspecific example of an image IM11; FIG. 19B is a view showing an imagein which a region equal to or greater than a first threshold isextracted from the image IM11 shown in FIG. 19A; FIG. 19C is a viewshowing an image in which a region equal to or greater than a secondthreshold larger than the first threshold is extracted from the imageIM11 shown in FIG. 19A; FIG. 19D is a view showing a specific example ofan image IM12; FIG. 19E is a view showing an image in which a regionequal to or greater than a first threshold is extracted from the imageIM12 shown in FIG. 19D; and FIG. 19F is a view showing an image in whicha region equal to or greater than a second threshold, larger than thefirst threshold, is extracted from the image IM11 shown in FIG. 19D.

FIG. 20 is a flow chart for explaining an operation of the imagematching apparatus according to the present embodiment.

FIG. 21 is a flow chart for explaining an operation of an image matchingapparatus of a fifth embodiment according to the present invention.

FIG. 22 is a flow chart for explaining an operation of an image matchingapparatus according to a sixth embodiment according to the presentinvention.

BEST MODE FOR CARRYING OUT THE INVENTION

FIG. 1 is a functional block diagram illustrated in a form of hardwareof an image matching apparatus of a first embodiment according to thepresent invention.

An image matching apparatus 1 according to the present embodiment, asshown in FIG. 1, has an image input unit 11, a memory 12, a FFTprocessing unit 13, a coordinates transform unit 14, a Hough transformunit 15, a CPU 16, and an operation processing unit 17.

For example, the image input unit 11 is connected to the memory 12. Thememory 12, the FFT processing unit 13, the coordinates transform unit14, the Hough transform unit 15, and the CPU 16 are connected by a busBS. The operation processing unit 17 is connected to the CPU 16.

The image input unit 11 functions as an input unit for inputting animage from the outside. For example, a registered image AIM and an imageto be compared with the registered image AIM (referred to an image to bematched RIM, also) are input to the image input unit 11.

The memory 12, for example, stores an image input from the image inputunit 11. The memory 12 stores the registered image AIM, the image to bematched RIM, and a program PRG as shown in FIG. 1.

The program PRG is executed, for example, by the CPU 16, and includesroutines for realizing functions concerning a transform processing, acorrelation processing, and a matching processing, according to thepresent invention.

The FFT processing unit 13, for example under control of the CPU 16,performs a two-dimensional Fourier transform processing to an imagestored in the memory 12, for example, and outputs the processed resultto the coordinates transform unit 14 and the CPU 16.

The coordinates transform unit 14, for example under control of the CPU16, performs a logarithm-polar coordinates transform processing to theresult of the two-dimensional Fourier transform processing performed bythe FFT processing unit 13, and outputs the coordinates transformedresult to the CPU 16.

The operation processing unit 17 performs a predetermined processingsuch as a release of an electric-key in the case where the registeredimage AIM and the image to be matched RIM are matched, for example,based on the processed result by the CPU 16 as mentioned later.

The Hough transform unit 15 performs a Hough transform processing asmentioned later under control of the CPU 16, and outputs the processedresult to the CPU 16. The Hough transform unit 15 is preferably usedwith an exclusive circuit formed by a hardware in order to perform, forexample, the Hough transform processing at a high speed.

The CPU 16 performs a matching processing to the registered image AIMand the image to be matched RIM stored in the memory 12, for example,based on the program PRG according to the present embodiment of thepresent invention. The CPU 16 controls, for example, the image inputunit 11, the memory 12, the FFT processing unit 13, the coordinatestransform unit 14, the Hough transform unit 15, and the operationprocessing unit 17, to realize the operation according to the presentembodiment.

FIG. 2 is a functional block diagram illustrated in a form of a softwareof the image matching apparatus shown in FIG. 1.

For example, the CPU 16 executes the program PRG stored in the memory 12to realize functions of a position correction unit 161, a Houghtransform unit 162, an extraction unit 163, and a judgment unit 164 asshown in FIG. 2.

The position correction unit 161 corresponds to a position correctionmeans according to the present invention, the Hough transform unit 162corresponds to a transform means according to the present invention, theextraction unit 163 corresponds to an extraction means according to thepresent invention, and the judgment unit 164 corresponds to a matchingmeans according to the present invention.

The position correction unit 161, for example, corrects a positionalshift to an image pattern in the registered image AIM and the image tobe matched RIM stored in the memory 12 in a right and left direction andan up and down direction, an enlargement ratio, and a rotation angleshift of the two images, and outputs the corrected image to the Houghtransform unit 162.

In detail, for example, the positional correction unit 161 performs aposition correction processing to the registered image AIM and the imageto be matched RIM and outputs the result as signals S1611 and S1612 tothe Hough transform unit 162.

The Hough transform unit 162, for example, executes the Hough transformprocessing in the Hough transform unit 15 performing the exclusive Houghtransform processing in hardware.

In detail, for example, the Hough transform unit 162 performs the Houghtransform processing to the signal S1611 which is the registered imageAIM to which the position correction processing was performed, andoutputs the processing result as the signal S1621.

The Hough transform unit 162 performs the Hough transform processing tothe signal S1612 which is the image to be matched RIM to which theposition correction processing was performed, and outputs the result asa signal S1622.

FIGS. 3A and 3B are views for explaining an operation of the Houghtransform unit shown in FIG. 2.

The Hough transform unit 162 performs an image processing, for example,to the first image and the second image respectively, by which thepoints in each image are transformed to curved patterns and by whichlinear components in each image are transformed to a plurality ofoverlapped curved patterns, based on a distance ρ0 from a referenceposition O to a shortest point P0 in a straight line L0 passing througha point in the image and an angle θ between a straight line n0 passingthough the reference position O and the shortest point P0 and areference axis including the reference position O, to thereby generate afirst transformed image and a second transformed image.

For simplifying explanations, for example, as shown in FIG. 3A, it isassumed that, in an x-y plane, there is the straight line L0, and aposition P (x1, y1), a position P2 (x2, y2), and a position P3 (x3, y3)on the straight line L0.

It is assumed that a straight line through the origin O andperpendicular to the straight line L0 is the line n0, so the straightline n0 and the x-axis as the reference axis are crossed at an angle θ0and that the origin O and the straight line L0 are separated with adistance |ρ0|, for example. Here, |ρ0| indicates the absolute value ofρ0. The straight line L0 can be expressed by parameters (ρ0, θ0).

The Hough transform processing performed to coordinates (x, y) in thex-y plane, is defined by formula (1), for example.

[Formula 1]

ρ=x·cos θ+y·sin θ  (1)

For example, when the Hough transform processing expressed by theformula (1) is performed to the positions P1, P2, and P3, the positionsP1, P2, and P3 are transformed to curved lines in a ρ-θ space as shownin FIG. 3B. In detail, by the Hough transform processing, the pointP1(x1, y1) is transformed to a curved line PL1(x1·cos θ+y1·sin θ), thepoint P2(x2, y2) is transformed to a curved line PL2(x2·cos θ+y2·sin θ),and the point P3(x3, y3) is transformed to a curved line PL3(x3·cosθ+y3·sin θ).

Patterns of the curved lines (curved patterns) PL1, PL2, and PL3 arecrossed at a crossed point CP(ρ0, θ0) in the ρ-θ space. The crossedpoint P(ρ0, θ0) in the ρ-θ space corresponds to the linear component L0in the x-y space.

Conversely, as shown in FIG. 3A, the linear component L0 on the x-yplane corresponds to the crossed point CP of the curved patterns PL1,PL2, and PL3 on the ρ-θ space.

As described above, the Hough transform processing is performed to atwo-binary image, it may judge which linear components dominates in thex-y plane before the transform processing, based on the degree of theoverlap of the curved patterns in the above resultant ρ-θ space.

FIGS. 4A to 4F are views for explaining an operation of the Houghtransform unit shown in FIG. 2.

The Hough transform unit 162 performs the Hough transform processing tothe registered image AIM to which the position correction processing wasperformed, shown in FIG. 4A for example, to generate the image S1621shown in FIG. 4C, and performs the Hough transform processing to theimage to be matched RIM to which the position correction processing wasperformed, shown in FIG. 4B, to generate the image S1622.

In the respective pixels included in the images S1621 and S1622, a valuecorresponding to the degree of the overlap of the curved patterns isset. In the present embodiment, in the images to be displayed based on apredetermined gradation, a pixel(s) in which the degree of the overlapof the curved patterns is large, is displayed in white.

As mentioned later, the matching unit 1642 performs a matchingprocessing based on the degree of the overlap of the curved patterns, soit performs the matching processing based on the linear component in theoriginal x-y space.

The extraction unit 163, from the respective first transformed image andthe second transformed image, extracts regions each of which indicatesthe degree of the overlap of the curved patterns in the transformedimage equal to or greater than a threshold set in advance.

In detail, the extraction unit 163, for example, extracts the region ofwhich the degree of the overlap of the curved patterns in thetransformed image is equal to or greater than the threshold set inadvance, from the signal S1621 as the first transformed image shown inFIG. 4C, generates an image S1631 shown in FIG. 4E, and outputs the sameto the matching unit 1642.

Also, the extraction unit 163, for example, extracts the region of whichthe degree of the overlap of the curved patterns in the transformedimage is equal to or greater than the threshold set in advance, from thesignal S1622 as the second transformed image shown in FIG. 4D, generatesan image S1632 shown in FIG. 4F, and outputs the same to the matchingunit 1642.

By performing the extraction processing, for example, noise componentsdifferent from the linear components in the registered image AIM and theimage to be matched RIM, in the x-y space, for example, pointcomponents, are deleted.

The judgment unit 164 performs the matching of the first image and thesecond image based on the degree of the overlap of the patterns in thefirst transformed image and the second transformed image and a matchingor mismatching of the patterns in the two images.

In detail, the judgment unit 164 performs the matching, for example, tothe signal S1631 and the signal S1632, and outputs the matched result asa signal S164.

The judgment unit 164, for example, as shown in FIG. 2, has a similaritygeneration unit 1641 and a matching unit 1642.

The similarity generation unit 1641, for example, performs a comparisonprocessing to a plurality of different positional relationships in thefirst transformed image and the second transformed image, and generatesa similarity as the correlation value based on the compared result.

In detail, the similarity generation unit 1641, for example, performsthe comparison processing to a plurality of different positionalrelationships in the two images of the signal S1631 and the signalS1632, and generates the similarity as the correlation value based onthe compared result.

For example, the similarity generation unit 1641, assuming the twoimages as f1(m, n) and f2(m, n), calculates a similarity Sim by applyingthe following formula (2) and outputs the result as S1641.

$\begin{matrix}\lbrack {{Formula}{\mspace{11mu} \;}2} \rbrack & \; \\{{{Sim}( {{f\; 1},{f\; 2}} )} = \frac{\sum\limits_{m = 0}^{M - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 1( {m,n} )f\; 2( {m,n} )}}}{\sqrt{\{ {\sum\limits_{m = 0}^{M - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 1( {m,n} )^{2}}}} \}}\sqrt{\{ {\sum\limits_{m = 0}^{M - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 2( {m,n} )^{2}}}} \}}}} & (2)\end{matrix}$

FIGS. 5A to 5C are views for explaining an operation of the similaritygeneration unit shown in FIG. 2.

The similarity generation unit 1641, for example, in the case where thesimilarity of two images including linear components (also, referred tolinear shapes) shown in FIGS. 5A and 5B is generated, generates thesimilarity corresponding to a number of the crossed points CP of the twoimages as shown in FIG. 5C. Here, for simplifying explanations, thelinear components are indicated by black pixels of a bit value ‘1’ andothers are indicated by white pixels of a bit value ‘0’.

The matching unit 1642 performs the matching of the image to be matchedRIM and the registered image AIM based on the signal S1641 indicatingthe similarity generated by the similarity generation unit 1641.

For example, the matching unit 1642 judges that the registered image AIMand the image to be matched RIM are matched in the case where thesimilarity is larger than a predetermined value.

FIG. 6 is a flow chart for explaining an operation of the image matchingapparatus according to the present embodiment shown in FIG. 1. Whilereferring to FIG. 6, the operation of the image matching apparatus,mainly an operation of the CPU, will be described.

For example, the registered image AIM and the image to be matched RIMare input from the image input unit 11, and the memory 12 stores therespective input data.

At step ST101, for example as shown in FIG. 2, the position correctionunit 161 performs the position correction processing based on the imagepatterns in the registered image AIM and the image to be matched RIMstored in the memory 12, in detail, corrects a positional shift in aright and left direction and an up and down direction, a magnificationratio, and the rotation angle shift in the two images, and outputs thecorrected images as the signals S1611 and S1612 to the Hough transformunit 162.

At step ST102, the Hough transform unit 162, for example, performs theimage processing to the signal S1611 being the registered image AIM towhich the position correction processing was performed shown in FIG. 4A,the image processing transforming the positions in each image to thecurved pattern PL and the linear components L in each image to aplurality of the overlapped curved patterns PL as shown in FIG. 3A,based on the distance ρ0 from the reference position O to the shortestpoint P0 in the straight line L0 passing through the point in the imageand the angle θ between the straight line n0 passing through thereference position O and the shortest point P0 and the x-axis as thereference axis including the reference position O, to generate thesignal S162 as the transformed image in the ρ-θ space as shown in FIG.4C.

The Hough transform unit 162 performs the Hough transform processing, inthe same way, to the signal S1612 being the image to be matched RIM towhich the position correction processing was performed as shown in FIG.4B, to generate the signal S1622 as the transformed image in the ρ-θspace as shown in FIG. 4D, for example.

At step ST103, the extraction unit 163 extracts the regions each ofwhich indicates the degree of the overlap of the curved patterns in thetransformed image equal to or greater than the threshold set in advance,from the transformed image S1621 and the transformed image S1622respectively.

In detail, as described above, the value corresponding to the degree ofthe overlap of the curved patterns is set in the respective pixels inthe images S1621 and S1622, and in the image to be displayed based on apredetermined gradation, portions of which the degree of the overlap ofthe curved patterns are high, are displayed in white.

The extraction unit 163, for example, extracts the region of which thedegree of the overlap of the curved patterns in the transformed imageS1621 shown in FIG. 4C is equal to or greater than the threshold set inadvance, generates the image S1631 shown in FIG. 4E for example, andoutputs the same to the matching unit 1642.

The extraction unit 163, for example, extracts the region of which thedegree of the overlap of the curved patterns in the transformed imageS1622 shown in FIG. 4D is equal to or greater than the threshold set inadvance, generates the image S1632 shown in FIG. 4F for example, andoutputs the same to the matching unit 1642.

The matching unit 1642 performs the matching of the image to be matchedRIM and the registered image AIM based on the degree of the overlap ofthe patterns in the transformed images S1631 and S1632 and the matchingor mismatching of the patterns in the transformed images S1631 andS1632.

In detail, at step ST104, the similarity generation unit 1641 performsthe comparison processing to a plurality of the different positionalrelationships in the transformed images S1631 and S1632, generates thesimilarity Sim as the correlation value based on the compared result,for example, as expressed by the formula (2), and outputs the same asthe signal S1641.

At step ST105, the matching unit 1642 performs the matching of the imageto be matched RIM and the registered image AIM based on the similaritySim functioning as the correlation value generated by the similaritygeneration unit 1641. In detail, the matching unit 1642 judges that theregistered image AIM and the image to be matched RIM are matched in thecase where the similarity Sim is greater than the threshold set inadvance, otherwise judges that they are mismatched in the case where thesimilarity Sim is equal to or less than the threshold.

For example, the operation processing unit 17 performs a predeterminedprocessing such as release of an electric-key in the case where theimage matching apparatus according to the present embodiment is appliedto a vein pattern matching apparatus in a security field.

As described above, the matching apparatus is provided with: the Houghtransform unit 15 performing the Hough transform processing to theregistered image AIM and the image to be matched RIM respectively, indetail, performing the image processing to the registered image AIM andthe image to be matched RIM, by which the points in each image aretransformed to the curved pattern PL and the linear components in eachimage are transformed to a plurality of the overlapped curved-patternsPL based on the distance ρ from the reference position O to the shortestpoint P0 in the straight line L passing through the point in the imageand the angle θ between the straight line n0 passing through thereference position O and the shortest point P0 and the x-axis as thereference axis including the reference position O, and generating thetransformed image S1621 and the transformed image S1622; and thejudgment unit 164 performing the matching of the image to be matched RIMand the registered image AIM based on the degree of the overlap of thepatterns in the transformed image S1621 and the transformed image S1622generated by the Hough transform unit 15 and the matching or mismatchingof the patterns in the transformed image S1621 and the transformed imageS1622. So, the matching images including the featured linear componentscan be performed at a high accuracy.

Namely, the Hough transform unit 15 generates the transformed imagesunder consideration of the linear components (also, referred to a linearshapes) in the registered image AIM and the image to be matched RIM byperforming the Hough transform processing, and the judgment unit 164performs the matching of the transformed images based on the degree ofthe overlap of the curved patterns. So, the matching images includingthe featured linear components can be performed at a high accuracy.

Further, if the extraction unit 163 is provided at a subsequent stage ofthe Hough transform unit 162, the extraction unit 163 extracts theregion of which the degree of the overlap of the curved patterns PL ineither the transformed image S1621 or the transformed image S1622 isequal to or greater than the threshold set in advance, namely, deletespoint components functioning as noise components on the registered imageAIM and the image to be matched RIM and extracts only the linearcomponents, and generates the images S1631 and S1632, and the judgmentunit 164 performs the matching of the image to be matched RIM and theregistered image AIM based on the matching or mismatching of thepatterns in the extracted region. Consequently, the matching processingfurther improved and at a higher accuracy can be performed.

And, the judgment unit 164 is provided with: the similarity generationunit 1641 performing the comparison processing to a plurality of thedifferent positional relationships in the transformed images, andgenerating the similarity Sim as the correlation value based on thecompared result; and the matching unit 1642 performing the matching ofthe image to be matched RIM and the registered image AIM based on thegenerated similarity Sim. Then, the judgment unit 164 generates thesimilarity as the correlation value by applying a simple calculation andperforms the matching processing based on the similarity. Consequently,the matching processing can be performed at a high speed.

In the present embodiment, though the position correction processing isperformed at step ST101, but, it is not limited thereto. For example, inthe case where the position correction processing is unnecessary, theHough transform unit 15 may perform the Hough transform processing tothe registered image AIM and the image to be matched RIM respectively.Therefore, a processing load is reduced and the matching processing canbe performed at a high speed.

In the present embodiment, though, at step ST103, the extraction unit163 extracts the region of which the degree of the overlap of the curvedpatterns PL is equal to or greater than the threshold set in advance,but, it is not limited thereto.

For example, the extraction unit 163 dose not perform the extractionprocessing, and the judgment unit 164 may perform the matching of theimage to be matched RIM and the registered image AIM based on the degreeof the overlap of the patterns in the image S1621 and the image S1622and the matching or mismatching of the patterns therein. Therefore, theprocessing load is reduced and the matching processing can be performedat a high speed.

FIG. 7 is a block diagram showing a position correction unit of theimage matching apparatus of a second embodiment according to the presentinvention.

The image matching apparatus 1 a according to the present embodiment, ina functional block diagram illustrated as a hardware, has approximatelythe same configuration as the image matching apparatus 1 according tothe first embodiment, and, for example, has the image input unit 11, thememory 12, the FFT processing unit 13, the coordinates transform unit14, the Hough transform unit 15, the CPU 16 and the operation processingunit 17 as shown in FIG. 1.

In the functional block illustrated as a software, the image matchingapparatus 1 a has approximately the same configuration as the imagematching apparatus 1 according to the first embodiment, and the CPU 16executes the program PRG to realize the function of the positioncorrection unit 161, the Hough transform unit 162, the extraction unit163, and the judgment unit 164.

Components the same as those of the above embodiment are assigned thesame notations (references) and explanations thereof are omitted. Andonly different points will be described.

The different points are the followings: the position correction unit161, as a position correction processing, generating the correlationvalue based on phase components which are results of the rotation anglecorrection processing of the registered image AIM and the image to bematched RIM or the enlargement ratio correction processing thereof, andthe Fourier transform processing thereof, and performing the positioncorrection processing to the registered image AIM and the image to bematched RIM based on the generated correlation value.

In detail, the position correction unit 161 according to the presentembodiment, as shown in FIG. 7, realizes functions of the magnificationinformation and rotation information unit 21, the correction unit 22,the phase-only correlation unit 23, the shift information generationunit 24, and the correction unit 25 by the CPU 16 executing the programPRG and controlling the FFT processing unit 13 and the coordinatestransform unit 14, for example.

The magnification information and rotation information unit 21 and thecorrection unit 22 perform a rotation angle correction processing or anenlargement ratio correction processing thereof to the registered imageAIM and the image to be matched RIM.

The magnification information and rotation information unit 21 generatesa magnification information and/or a rotation information based on theregistered image AIM and the image to be matched RIM, and outputs thesame as a signal S21 to the correction unit 22.

The magnification information includes an information indicating anenlargement and/or reduction ratio of the registered image AIM and theimage to be matched RIM. The rotation information includes aninformation indicating a rotation angle of the registered image AIM andthe image to be matched RIM.

In detail, for example, the magnification information and rotationinformation unit 21 has a Fourier and Mellin transform unit 211, aphase-only correlation unit 212, and a magnification information androtation information generation unit 213.

The Fourier and Mellin transform unit 211 performs a Fourier and Mellintransform as mentioned later to the respective image information andoutputs signals SA211 and SR211 indicating the respective transformedresults to the phase-only correlation unit 212.

In detail, the Fourier and Mellin transform unit 211 has Fouriertransform units 21111 and 21112, logarithmic transform units 21121 and21122, and logarithm-polar coordinates transform units 21131 and 21132.

The Fourier transform unit 21111 performs Fourier transform as expressedby the following formula (3) to generate a Fourier image data F1(u, v)when the registered image AIM is assumed as f1(m, n) in the case wherethe registered image AIM is N×N pixel image, for example, and outputsthe same to the logarithmic transform unit 21121. The Fourier transformunit 21112 performs Fourier transform as expressed by the followingformula (4) to generate a Fourier image data F2(u, v) when the image tobe matched RIM is assumed as f2(m, n) in the case where the image to bematched RIM is N×N pixel image, for example, and outputs the same to thelogarithmic transform unit 21122.

The Fourier image data F1(u, v) is formed by an amplitude spectrum A(u,v) and a phase spectrum Θ(u, v) as expressed by the following formula(3). The Fourier image data F2(u, v) is formed by an amplitude spectrumB(u, v) and a phase spectrum Φ(u, v) as expressed by the followingformula (4).

$\begin{matrix}\lbrack {{Formula}\mspace{14mu} 3} \rbrack & \; \\\begin{matrix}{{F\; 1( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 1( {m,n} )^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{A( {u,v} )}^{{j\Theta}{({u,v})}}}}\end{matrix} & (3) \\\lbrack {{Formula}\mspace{14mu} 4} \rbrack & \; \\\begin{matrix}{{F\; 2( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 2( {m,n} )^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{B( {u,v} )}^{{j\Phi}{({u,v})}}}}\end{matrix} & (4)\end{matrix}$

The logarithmic transform units 21121 and 21122 perform logarithmicprocessings to amplitude components of the Fourier image data F1(u, v)and F2(u, v) generated by the Fourier transform units 21111 and 21112.The logarithmic processing to the amplitude components is carried out toemphasize a high frequency component including a detail featuredinformation of the image data.

In detail, the logarithmic transform unit 21121 performs the logarithmicprocessing to the amplitude components A(u, v) as expressed by thefollowing formula (5) to generate A′(u, v), and outputs the same tologarithm-polar coordinates transform unit 21131. The logarithmictransform unit 21122 performs the logarithmic processing to theamplitude components B(u, v) as expressed by the following formula (6)to generate B′(u, v), and outputs the same to logarithm-polar coordinatetransform unit 21132.

[Formula 5]

A′(u,v)=log(|A(u,v)|+1  (5)

[Formula 6]

B′(u,v)=log(|B(u,v)|+1  (6)

The logarithm-polar coordinates transform units 21131 and 21132transform the signals output from the logarithmic transform units 21121and 21122 to signals of a logarithm-polar coordinates system (forexample, (log(r), Θ).

Generally, for example, when a position (x, y) is defined such as thefollowing formulas (7) and (8), μ is equal to log(r) when R is equal toe^(μ), so there is unanimously (log(r), Θ) corresponding to any point(x, y). The logarithm-polar coordinates transform units 21131 and 21132perform a coordinate transform processing based on the above nature.

[Formula 7]

x=e^(μ) cos

  (7)

[Formula 8]

y=e ^(μ) sin

(0≦

≦2π)  (8)

In detail, the logarithm-polar coordinates transform units 21131 and21132 define a set (r_(i), θ_(j)) expressed by the following formula (9)and a function f(r_(i), θ_(j)) expressed by the following formula (10).

$\begin{matrix}\lbrack {{formula}{\mspace{11mu} \;}9} \rbrack & \; \\{{{( {r_{i},\theta_{j}} ):r_{i}} = {\frac{1}{2}N^{i/N}}},{\theta_{j} = {\frac{2\; \pi \; j}{N} - \pi}}} & (9)\end{matrix}$

[formula 10]

f(r _(i),θ_(j))=(r _(i) cos θ_(j) +N/2,r _(i) sin θ_(j) +N/2)

(i=0,1, . . . , N−1,j=0,1, . . . , N−1)  (10)

The logarithm-polar coordinates transform units 21131 and 21132 performthe logarithm-polar coordinates transform to the image data A′(u, v) andB′(u, v) as expressed by the following formulas (11) and (12) byapplying the set (r_(i), θ_(j)) and the function f(r_(i), θ_(j)) definedby the formulas (9) and (10) to generate pA(r_(i), θ_(j)) and pB(r_(i),θ_(j)), and output the same as the signal SA211 and the signal SR211 tothe phase-only correlation unit 212.

[formula 11]

pA(r _(i),θ_(j))=A′(f(r _(i),θ_(j)))  (11)

[formula 12]

pB(r _(i),θ_(j))=B′(f(r _(i),θ_(j)))  (12)

FIG. 8 is a view for explaining an operation of the Fourier and Mellintransform unit 211 shown in FIG. 7.

The image f1(m, n) and the image f2(m, n), for example, includerectangle regions W1 and W2 having different predetermined angles withrespect to x-axis and y-axis.

In the Fourier and Mellin transform unit 211, for example as shown inFIG. 8, the Fourier transform processing is performed to the image f1(m,n) by the Fourier transform units 21111 to thereby generate a Fourierimage data F1(u, v), and the image data pA(r, θ) is generated by thelogarithmic transform unit 21121 and the logarithm-polar coordinatestransform unit 21131.

In the same way, the Fourier transform processing is performed to theimage f2(m, n) by the Fourier transform units 21112 to thereby generatea Fourier image data F2(u, v), and the image data pB(r, θ) is generatedby the logarithmic transform unit 21122 and the logarithm-polarcoordinates transform unit 21132.

As described above, the images f1(m, n) and f2(m, n) are transformedfrom Cartesian coordinates to the logarithm-polar coordinates system(also, referred to a Fourier and Mellin space) by the Fourier transformand the logarithm-polar coordinates transform.

In the Fourier and Mellin space, there is a nature that the component ismoved along a log-r axis based on a scaling of an image and moved alonga θ-axis based on a rotation angle of the image.

By applying the above nature, the scaling (magnification information)and a rotation angle of the images f1(m, n) and f2(m, n) may be obtainedbased on an amount of the shift along the log-r axis in the Fourier andMellin space and an amount of the shift along the θ-axis.

The phase-only correlation unit 212, for example, applies a phase onlycorrelation method used in a phase-only filter (symmetric phase-onlymatched filter: SPOMF) to the signal SA211 and the signal SR211respectively indicating a pattern data output from the Fourier andMellin transfer 211 and obtains the amount of the parallel movementthereof.

The phase-only correlation unit 212, as shown in FIG. 7, has Fouriertransform units 2120 and 2121, a combination (synthesizing) unit 2122, aphase extraction unit 2123, and an inverse-Fourier transform unit 2124.

The Fourier transform units 2120 and 2121 perform Fourier transform tothe signals. SA211 (pA(m, n)) and SR211 (pB(m, n)) output from thelogarithm-polar coordinates transform units 21131 and 21132 by applyingthe following formulas (13) and (14). Here, X(u, v) and Y(u, v) indicateFourier coefficients. The Fourier coefficient X(u, v) is formed by anamplitude spectrum C(u, v) and a phase spectrum θ(u, v) as expressed bythe following formula (13). The Fourier coefficient Y(u, v) is formed byan amplitude spectrum D(u, v) and a phase spectrum φ(u, v) as expressedby the following formula (14).

$\begin{matrix}\lbrack {{formula}{\mspace{11mu} \;}13} \rbrack & \; \\\begin{matrix}{{X( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {{{pA}( {m,n} )}^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{C( {u,v} )}^{{j\theta}{({u,v})}}}}\end{matrix} & (13) \\\lbrack {{formula}{\mspace{11mu} \;}14} \rbrack & \; \\\begin{matrix}{{Y( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {{{pB}( {m,n} )}^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{D( {u,v} )}^{{j\varphi}{({u,v})}}}}\end{matrix} & (14)\end{matrix}$

The combination unit 2122 combines X(u, v) and Y(u, v) generated by theFourier transform units 2120 and 2121, and obtains the correlation. Forexample, the combination unit 2122 generates X(u, v)·Y*(u, v) andoutputs the same to the phase extraction unit 2123. Here, Y*(u, v) isdefined as a complex conjugate of Y(u, v).

The phase extraction unit 2123 deletes an amplitude component based on acombined signal output from the combination unit 2122 and extracts aphase information.

For example, the phase extraction unit 2123 extracts the phase componentwhich is Z(u, v)=e^(j(θ(u, v)−φ(u, v))) based on the X(u, v)Y*(u, v).

The extraction of the phase information is not limited to the above. Forexample, based on the output from the Fourier transform units 2120 and2121 and the following formulas (15) and (16), the phase information isextracted and only the phase components are combined as expressed by thefollowing formula (17) to generate Z(u, v).

[formula 15]

X′(u,v)=e ^(jθ(u,v))  (15)

[formula 16]

Y′(u,v)=e ^(jφ(u,v))  (16)

[formula 17]

Z(u,v)=X′(u,v)(Y′(u,v))=e ^(j(θ(u,v)−φ(u,v))  (17)

The inverse-Fourier transform unit 2124 performs the inverse-Fouriertransform processing to the signal Z(u, v) including the only phaseinformation and output from the phase extraction unit 2123 to generate acorrelation intensity image.

In detail, the inverse-Fourier transform unit 2124 performs theinverse-Fourier transform processing on the signal Z(u, v) as expressedby the following formula (18) to generate a correlation intensity imageG(p, q).

$\begin{matrix}\lbrack {{formula}\mspace{20mu} 18} \rbrack & \; \\\begin{matrix}{{G( {p,q} )} = {\sum\limits_{u = 0}^{N - 1}\; {\sum\limits_{v = 0}^{N - 1}\; {( {Z( {u,v} )} )^{j\; 2\; {\pi {({{({{up} + {vq}})}/N})}}}}}}} \\{= {\sum\limits_{u = 0}^{N - 1}\; {\sum\limits_{v = 0}^{N - 1}\; {( ^{j{({{\theta {({u,v})}} - {\varphi {({u,v})}}})}} )^{j\; 2\; {\pi {({{({{up} + {vq}})}/N})}}}}}}}\end{matrix} & (18)\end{matrix}$

The magnification information and rotation information generation unit213 generates a correction information S21 including a data indicating amagnification information (enlargement and/or reduction ratio) and arotation angle information of the image to be matched RIM to theregistered image AIM by detecting the amount of shift between an imagecenter and a peak position in the correlation intensity image G(p, q)generated by the inverse-Fourier transform unit 2124. The amount of theshift is equal to the amount of the parallel shift between theregistered image AIM and a pattern data obtained by performing theFourier-Mellin transform to the image to be matched RIM.

The correction unit 22 corrects the image to be matched RIM based on thecorrection information S21 output from the magnification information androtation information generation unit 213 in the magnificationinformation and rotation information unit 21. In detail, the correctionunit 22 performs an enlargement and/or reduction processing and arotation processing to the image to be matched RIM based on amagnification information and the rotation angle information included inthe correction information S21, and outputs the result to the phase-onlycorrelation unit 23. By the correction processing of the correction unit22, a scaling difference and a rotation component difference between theregistered image AIM and the image to be matched RIM are deleted.

Therefore, only a parallel movement component remains as the differencebetween the registered image AIM and the image to be matched RIM towhich the correction processing was performed.

The phase-only correlation unit 23 detects the parallel movementcomponent between the registered image AIM and the image to be matchedRIM to which the correction processing was performed, and also detectsthe correlation value thereof. The detection processing, for example, isperformed by applying the phase-only correlation method used in thephase-only filter described above.

In detail, the phase-only correlation unit 23 has Fourier transformunits 2311 and 2312, a combination unit 232, a phase extraction unit233, and an inverse-Fourier transform unit 234.

The Fourier transform units 2311 and 2312, the combination unit 232, thephase extraction unit 233, and the inverse-Fourier transform unit 234have respectively the same functions as the Fourier transform units 2120and 2121, the combination unit 2122, the phase extraction unit 2133, andthe inverse-Fourier transform unit 2124 of the phase-only correlationunit 212 and explanations will be simplified.

The Fourier transform unit 2311 performs the Fourier transformprocessing to the registered image AIM, and outputs the result to thecombination unit 232. In the above case, the registered image AIM towhich the Fourier transform processing was performed by the Fouriertransform unit 21111 may be stored in the memory 12, and output to thecombination unit 232. Consequently, the Fourier transform is notperformed two times, so the processing is reduced.

The Fourier transform units 2312 performs the Fourier transformprocessing to the image S22 corrected by the correction unit 22, andoutputs the result of a Fourier image to the combination unit 232.

The combination unit 232 combines the Fourier images S2311 and S2312output from the Fourier transform units 2311 and 2312, and outputs thecombined image S232 to the phase extraction unit 233.

The phase extraction unit 233 extracts the phase information based onthe combined image S232 as described above, and outputs a signal S233 tothe inverse-Fourier transform unit 234.

The inverse-Fourier transform unit 234 performs the inverse-Fouriertransform processing to the signal S233 to thereby generate acorrelation intensity image (correlation image data), and outputs thesame as a signal S23 to the shift information generation unit 24.

A processing for detecting the amount of the parallel movement in theabove phase-only correlation method will be described in detail.

For example, the Fourier transform processing is performed to theoriginal image f1(m, n), the original image f2(m, n), and an image f3(m,n)=f2(m+α, n+β) in which the image f2(m, n) is moved in parallel, tothereby generate Fourier coefficients F1(u, v), F2(u, v), and F3(u, v)as expressed by the following formulas (19) to (21).

$\begin{matrix}\lbrack {{formula}\mspace{14mu} 19} \rbrack & \; \\\begin{matrix}{{F\; 1( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 1( {m,n} )^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{A( {u,v} )}^{{j\Theta}{({u,v})}}}}\end{matrix} & (19) \\\lbrack {{formula}\mspace{14mu} 20} \rbrack & \; \\\begin{matrix}{{F\; 2( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 2( {m,n} )^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{B( {u,v} )}^{{j\Phi}{({u,v})}}}}\end{matrix} & (20) \\\lbrack {{formula}{\mspace{11mu} \;}21} \rbrack & \; \\\begin{matrix}{{F\; 3( {u,v} )} = {\sum\limits_{m = 0}^{N - 1}\; {\sum\limits_{n = 0}^{N - 1}\; {f\; 2( {{m + \alpha},{n + \beta}} )^{{- {j2}}\; {\pi {({{({{mu} + {nv}})}/N})}}}}}}} \\{= {{B( {u,v} )}^{j{({{\Phi {({u,v})}} + {2\; {{\pi {({{\alpha \; u} + {\beta \; v}})}}/N}}})}}}}\end{matrix} & (21)\end{matrix}$

Based on the Fourier coefficients F1(u, v) to F3(u, v), phase imagesF′1(u, v) to F′3(u, v) including only the phase information aregenerated as expressed by the following formulas (22) to (24).

[formula 22]

F′1(u,v)=e ^(jθ(u,v))  (22)

[formula 23]

F′2(u,v)−e ^(jθ(u,v))  (23)

[formula 24]

F′3(u,v)=e ^(j(φ(u,v)+2π(αu+βv)/N))  (24)

A phase image correlation Z12(u, v) as the correlation between the phaseimage F′1(u, v) and the phase image F′2(u, v) is calculated as expressedby the following formula (25). A phase image correlation Z13(u, v) asthe correlation between the phase image F′1(u, v) and the phase imageF′3(u, v) is calculated as expressed by the following formula (26).

[formula 25]

Z12(u,v)=F′1(u,v)(F′2(u,v))*=e ^(j(θ(u,v)−φ(u,v)))  (25)

[formula 26]

Z13(u,v)=F′1(u,v)(F′3(u,v))*=e ^(j(θ(u,v)−φ(u,v)−2π(αu+βv)/N))  (26)

A correlation intensity image G12(r, s) of the correlation Z12(u, v) anda correlation intensity image G13(r, s) of the correlation Z13(u, v) arecalculated as expressed by the following formulas (27) and (28).

$\begin{matrix}\lbrack {{formula}{\mspace{11mu} \;}27} \rbrack & \; \\\begin{matrix}{{G\; 12( {r,s} )} = {\sum\limits_{u = 0}^{N - 1}\; {\sum\limits_{v = 0}^{N - 1}\; {( {Z\; 12( {u,v} )} )^{{j2}\; {\pi {({{({{ur} + {vs}})}/N})}}}}}}} \\{= {\sum\limits_{u = 0}^{N - 1}\; {\sum\limits_{v = 0}^{N - 1}\; {( ^{j{({{\Theta {({u,v})}} - {\Phi {({u,v})}}})}} )^{{j2}\; {\pi {({{({{ur} + {vs}})}/N})}}}}}}}\end{matrix} & (27) \\\lbrack {{formula}{\mspace{11mu} \;}28} \rbrack & \; \\\begin{matrix}{{G\; 13( {r,s} )} = {\sum\limits_{u = 0}^{N - 1}\; {\sum\limits_{v = 0}^{N - 1}\; {( {Z\; 13( {u,v} )} )^{{j2}\; {\pi {({{({{ur} + {vs}})}/N})}}}}}}} \\{= {\sum\limits_{u = 0}^{N - 1}\; {\sum\limits_{v = 0}^{N - 1}\; {( ^{j{({{\Theta {({u,v})}} - {\Phi {({u,v})}} - {2\; {{\pi {({{\alpha \; u} + {\beta v}})}}/N}}})}} )^{{j2}\; {\pi {({{({{ur} + {vs}})}/N})}}}}}}} \\{= {G\; 12( {{r - \alpha},{s - \beta}} )}}\end{matrix} & (28)\end{matrix}$

As expressed by the above formulas (27) and (28), in the case where theimage f3(m, n) is shifted to (+α, +β) in comparison with the image 2(m,n), a correlation intensity peak is generated at a position shifted to(−α, −β) in the correlation intensity image in the phase-onlycorrelation method. The amount of the parallel movement between the twoimages can be obtained based on the position of the correlationintensity position.

Further, by applying the phase-only correlation method in the Fourierand Mellin space, the amount of the parallel movement in the Fourier andMellin space can be detected. The amount of the parallel movement, asdescribed above, corresponds to the magnification information and therotation angle information in a real space.

FIGS. 9A to 9F are views for explaining different points between theself correlation method and the phase-only correlation method.

In the self correlation method, as shown in FIGS. 9A and 9B, the Fouriertransform is performed to an image IM1 and an image IM2 the same as theimage IM1 to thereby generate a self correlation function SG1. As shownin FIG. 9C, correlation intensity distributions including a highcorrelation intensity peak and a low correlation intensity peak aroundit, are obtained. In FIG. 9C, an ordinate (z-axis) indicates thecorrelation intensity, and an x-axis and a y-axis indicate the amount ofthe shift.

Otherwise, in the phase-only correlation method described above, asshown in FIGS. 9D and 9E, the Fourier transform is performed to theimage IM1 and the image IM2 the same as the image IM1, and only thephase information is correlated, as a result, correlation distributionsincluding only a high and sharp correlation intensity peak are obtainedas shown in FIG. 9F. In this way, the performance of the phase-onlymethod can obtain a precise information concerning the correlation incomparison with the self correlation method. In FIG. 9F, an ordinate(z-axis) indicates the correlation intensity, and an x-axis and a y-axisindicate the amounts of the shifts.

FIGS. 10A to 10C are views for explaining the correlation intensitydistributions in the case where the parallel movement shift existsbetween two images, in the phase-only correlation method.

For example, in the correlation intensity distributions by applying thephase-only method to the image IM1 and an image IM3 in which a pluralityof pixels are moved in parallel from the image IM1, shown in FIGS. 10Aand 10B, a high and sharp correlation intensity peak is distributed at aposition shifted from a peak position with a distance depending on theamount of the parallel movement in a correlation image data as shown inFIG. 9F. However, the peak intensity shown in FIG. 10C is smaller thanthat shown in FIG. 9F. This is because pixel regions where the imagesIM1 and IM3 are matched are smaller than that of the images IM1 and IM2.

FIGS. 11A to 11C are views for explaining the correlation intensitydistributions in the case where the rotation shift exists between twoimages, in the phase-only correlation method.

In the correlation intensity distributions by applying the phase-onlymethod to the image IM1 and an image IM4 rotating several degrees fromthe image IM1, shown in FIGS. 11A and 11B, for example, a weakcorrelation intensity distribution is obtained as shown in FIG. 11C. Inthe case where the phase-only correlation method is simply applied, itis difficult to detect the correlation due to the rotation shift.

Therefore, in the image matching apparatus 1 a according to the presentembodiment, the Fourier and Mellin transform is performed to theregistered image AIM and the image to be matched RIM, the amount of theparallel movement is detected in the Fourier and Mellin space byapplying the phase-only correlation method, and the magnificationinformation and the rotation angle information of the registered imageAIM and the image to be matched RIM are detected. The magnification andthe rotation angle of the image to be matched are corrected based on theabove information.

The parallel movement shift between the corrected image to be matchedRIM and the registered image AIM is detected by applying the phase-onlycorrelation method, and simultaneously, the matching of the images areperformed based on the correlation peak.

FIGS. 12A to 12C are views for explaining a correlation image dataoutput by the phase-only correlation unit 23. FIG. 12A shows an exampleof the registered image AIM, FIG. 12B shows an example of the image tobe matched RIM, and FIG. 12C shows a specific example of the correlationimage data. FIG. 13 is a view for explaining the correlation image datashown in FIG. 12C.

In the case where a binary pattern and a linear shaped pattern of ahand-written character or a blood vessel pattern are included in theregistered image AIM and the image to be matched RIM, for example, inthe case of the registered image AIM and the image to be matched RIMshown in FIGS. 12A and 12B, the phase-only correlation unit 23 outputs acorrelation image data shown in FIG. 12C as the signal S23.

In FIG. 12C, a z-axis direction indicates a correlation peak intensityin the correlation image data, and the correlation peak intensitycorresponds to the degree of the correlation between the registeredimage AIM and the image to be matched RIM. As shown in FIG. 12C and FIG.13, in a correlation peak position PP in a x-axis direction and a y-axisdirection, for example, the amount of the shift from a image centercorresponds to the amount of the parallel movement between theregistered image AIM and the image to be matched RIM.

The shift information generation unit 24 generates the amount of theshift from the image center as a shift information based on the signalS23 output from the phase-only correlation unit 23, and outputs a signalS24 to the correction unit 25.

The correction unit 25 performs a position correction processing to theimage to be matched RIM based on the image to be matched RIM and thesignal S24 as the shift information output from the shift informationgeneration unit 24, and outputs the resultant as a signal S1612.

FIG. 14 is a flow chart for explaining an operation of the imagematching apparatus 1 shown in FIG. 1. The operation of the imagematching apparatus 1 a having the configuration described above will bedescribed with reference to FIG. 14 in terms of a different point fromthe first embodiment.

For example, the registered image AIM and the image to be matched RIMare input from the image input unit 11, and the respective image dataare stored in the memory (ST1).

Here, in order to obtain the magnifications (enlargement and/orreduction ratio) and the rotation angle information of the image to bematched RIM to the registered image AIM, the registered image AIM isread out from the memory 12 (ST2), and applied with the Fouriertransform processing in the Fourier transform unit 21111 in themagnification information and rotation information unit 21 (ST3), and agenerated Fourier image data S21111 is stored or recorded in the memory12 (ST4).

An amplitude component in the Fourier image data S21111 is applied withthe logarithmic processing by the logarithmic transform unit 21121, andtransformed to a logarithm-polar coordinates system by thelogarithm-polar coordinates transform unit 21131 (ST5).

The image to be matched RIM is read out from the memory 12 (ST6), andapplied with the Fourier transform by the Fourier transform unit 21112in the same way (ST7). The amplitude component in the Fourier image dataS21112 is applied with the logarithmic processing by the logarithmictransform unit 21122, and transformed to a logarithm-polar coordinatessystem by the logarithm-polar coordinates transform unit 21132 (ST8).

Image signals (also, referred to pattern data) SA211 and SR211, obtainedby performing the Fourier and Mellin transform to the above registeredimage AIM and the image to be matched RIM, are respectively applied withthe Fourier transform processing by the Fourier transform units 2120 and2121 of the phase-only correlation unit 212 (ST9), and combined by thecombination unit 2122 (ST10). The amplitude component is deleted fromthe combined signal by the phase extraction unit 2123 (ST11), theresidual phase component is applied with the inverse-Fourier transformprocessing by the inverse-Fourier transform unit 2124 (ST12), and acorrelation information including the magnification, information and therotation information is generated (detected) by the magnificationinformation and rotation information generation unit 213, based on theamount of the shift between the image center and the peak position ofthe obtained correlation image data (ST13).

The correction unit 22 performs the correction processing forenlargement and/or reduction and the rotation processing to the image tobe matched based on the correction information to thereby delete thescaling component and the rotation component between the images (ST14).The residual difference is only the parallel movement component, whichis detected by applying the phase-only correlation method.

The image to be matched RIM to which the correction processing wasperformed, is applied with the Fourier transform by the Fouriertransform unit 2312 of the phase-only correlation unit 23 (ST15) tothereby generate a Fourier image data S2312, the registered image AIMstored in the memory 12 and applied with the Fourier transformprocessing, is read out (ST16), and a combined data S232 is generated bythe combination unit 232 (ST17).

In the above case, the registered image AIM may be applied with theFourier transform processing in the Fourier transform unit 2311 tothereby generate the Fourier image data S2311, and output the same tothe combination unit 232.

The amplitude information in the combined data S232 is deleted by thephase extraction unit 233 (ST18), the residual phase information isinput to the inverse-Fourier transform unit 234, and a signal S23 isoutput as the correlation image data (ST19).

Based on the signal 23, the amount of the shift of the peak position isdetected by the shift information generation unit 24, and the signal S24is output as the shift information (ST20).

In the correction unit 25, the position correction processing isperformed to the registered image AIM and the image to be matched RIMbased on the signal S24 as the shift information and the image to bematched RIM, then a signal S1612 is output. Also, the registered imageAIM is output as a signal S1611 (ST21).

The following operations are the substantially same as the firstembodiment, the Hough transform unit 162 performs the Hough transformprocessing (ST22), the extraction unit 163 performs the extractionprocessing (ST23), and the similarity generation unit 1641 generates thesimilarity Sim (ST24).

At step ST25, the matching unit 1642 judges whether or not thesimilarity Sim is larger than (or exceeds) the threshold set in advance.If it is larger than the threshold, it is judged that the registeredimage AIM and the image to be matched RIM are matched (ST26), otherwise,if it is smaller than the threshold, it is judged that they aremismatched (ST27).

As described above, in the present embodiment, the position correctionunit 161, as the position correction processing, generates thecorrelation value based on the phase components which are results of therotation angle correction processing or the magnification ratiocorrection processing and the Fourier transform processing to theregistered image AIM and the image to be matched RIM, and performs theposition correction processing to the registered image AIM and the imageto be matched RIM based on the generated correlation value,consequently, the position correction processing can be performed at ahigh accuracy, as a result, the matching processing can be performed ata high accuracy.

FIG. 15 is a functional block diagram of an image matching apparatus ofa third embodiment according to the present invention.

An image matching apparatus 1 b according to the present embodiment, inthe functional block diagram illustrated as a hardware, has theapproximately same configuration as the image matching apparatus 1according to the first embodiment, and, for example, has the image inputunit 11, the memory 12, the FFT processing unit 13, the coordinatetransform unit 14, the Hough transform unit 15, the CPU 16, and theoperation processing unit 17 as shown in FIG. 1.

The image matching apparatus 1 b, in the functional block diagramillustrated as a software, has the approximately same configuration asthe image matching apparatus 1 according to the second embodiment, andthe CPU 16 executes the program PRG to realize functions of a positioncorrection unit 161 b, the Hough transform unit 162, the extraction unit163, and a judgment unit 164 b. In FIG. 15, components the same as thoseof the above embodiments are assigned the same notations andexplanations thereof are omitted. And only different points will bedescribed.

A large different point is the following: the position correction unit161 b and the judgment unit 164 b being different.

In the present embodiment, the position correction unit 161 b performsthe correlation processing to the registered image AIM and the image tobe matched RIM to generate a plurality of the correlation valueindicating the correction position, and performs a plurality of theposition correction processing to the registered image AIM and the imageto be matched RIM based on the generated correlation value.

FIGS. 16A and 16B are views for explaining an operation of the positioncorrection unit shown in FIG. 15. Numerical values indicate thecorrelation peak intensities of the correlation image data in the x-yplane.

For example, in the case where the registered image AIM and the image tobe matched RIM include the binary linear component (linear shape)pattern, the correlation peak intensity (also, referred to a correlationintensity) is small value as shown in FIGS. 16A and 16B even if thecorrelation of the images is large.

For example, based on the signal S23, the shift information generationunit 24 of the position correction unit 161 b specifies N number of thecorrelation intensities successively from the top as shown in FIG. 16A,for example, 8 of the correlation value and the correlation peakpositions in the present embodiment, as an object of a positionalrelationship between the registered image AIM and the image to bematched RIM on a two-dimension.

The position correction unit 161 b performs the plurality of theposition correction processing if necessary, to a plurality of thecorrelation values and the position of the correlation peakscorresponding to the above values.

The Hough transform unit 162 performs the Hough transform processing tothe registered image AIM and the image to be matched RIM which areresults of the plurality of the position correction processing tothereby generate the transformed images S1621 and S1622.

The judgment unit 164 b generates the correlation value based onpatterns in the two transformed images, and performs the matchingprocessing to the registered image AIM and the image to be matched RIMbased on the generated correlation value and the threshold set inadvance. The judgment unit 164 b performs the matching processing to theresult of the plurality of the position correction processing based onthe total amount of the correlation value corresponding to differentpositions and the threshold set in advance.

In detail, the judgment unit 164 b has the similarity generation unit1641, a matching unit 1642 b, and the accumulation unit 1643.

The similarity generation unit 1641, for example, generates thesimilarity Sim by applying the above formula (2) based on the signalsS1631 and S1632 as described above, and outputs the same as a signalS1641 to the accumulation unit 1643 and the matching unit 1642 b.

The accumulation unit 1643 accumulates the similarity Sim based on thesignal S1641, and outputs the accumulated result as a signal S1643 tothe matching unit 1642 b.

The matching unit 1642 b performs the matching of the image to bematched RIM and the registered image AIM based on the signal S1641 andthe signal S1643.

The different point from the matching unit 1642 according to the firstembodiment is the followings: the matching unit 1642 b judging that theregistered image AIM and the image to be matched RIM are matched whenthe signal S1643 as the accumulation value of the similarity Simgenerated by the accumulation unit is larger than the predeterminedthreshold.

FIG. 17 is a flow chart for explaining an operation of the imagematching apparatus of the third embodiment according to the presentembodiment. While referring to the FIG. 14 and FIG. 17, the operation ofthe image matching apparatus, mainly the operation of the CPU 16, willbe described in terms of the different points from the first embodimentand the second embodiment.

Steps ST1 to ST19 are the same operations as the second embodiment shownin FIG. 14 and explanations will be omitted.

Based on the correlation image data S23 output from the phase-onlycorrelation unit 23 at step ST19, N number of objects P_(i) (P₀, P₁, P₂,. . . , P_(n-1)) are extracted successively from the upper side of thecorrelation peak intensity in the correlation image data S23 by theshift information generation unit 24 (ST28).

At step ST29, the accumulation unit 1643 initializes variables foraccumulation. For example, the variable i is initialized to 0 and theaccumulation value S is initialized to 0.

The correction unit 25 performs the position correction processing tothe registered image AIM and the image to be matched RIM based on therespective objects (coordinates) P_(i) and the amount of the shiftcorresponding to the object from the center position and the correlationimage data (ST30).

As described above, the Hough transform processing is performed to theregistered image AIM and the image to be matched RIM by the Houghtransform unit 162 (ST31), the extraction processing is performed by theextraction unit 163 (ST32), and the similarity Sim(i) is calculated bythe similarity generation unit 1641 and output to the accumulation unit1643 and the matching unit 1642 b (ST33).

The matching unit 1642 b compares the similarity Sim(i) with a firstthreshold th1 set in advance. In the case where the similarity Sim(i) issmaller than the first threshold (ST34), the accumulation unit 1643accumulates the similarity Sim(i), in detail, accumulates the same byformula S=S+Sim(i) and outputs the result to the matching unit 1642 b(ST35).

At step ST35, the matching unit 1642 b compares the accumulation value Swith a second threshold th2 set in advance (ST36), the variable i and avalue N−1 are compared in the case where the accumulation value S issmaller than the second threshold th2 (ST27), the variable i is addedwith one in the case where the variable i dose not matched N−1 (ST28),and the routine returns to step ST30. At step ST27, in the case wherethe variable i matches N−1, the images are mismatched (ST39).

On the other hand, in the comparison processing at step ST34, thematching unit 1642 b judges that the images are matched in the casewhere the similarity Sim(i) is equal to or larger than the firstthreshold. In the comparison processing at step ST36, the matching unit1642 b judges that the images are matched in the case where theaccumulated value S is equal to or larger than the second threshold th2(ST40). For example, in the case where the image matching apparatusaccording to the present embodiment is applied to a vein patternmatching apparatus in a security field, the operation processing unit 17performs a processing such that the electric key is released.

As described above, in the present embodiment, the position correctionunit 161 generates a plurality of the correlation value indicating thepositions to be corrected, performs the plurality of the positioncorrection processing to the registered image AIM and the image to bematched RIM based on the generated correlation value. The Houghtransform unit 162 performs the Hough transform processing as an imageprocessing to the registered image AIM and the image to be matched RIMwhich are results of the plurality of the position correctionprocessings. And the matching unit 1642 b performs the matchingprocessing to the accumulation value of the similarity as thecorrelation value corresponding to the pattern in each of thetransformed images. Consequently, for example even if the correlationbetween the two image data to be compared is small, the similaritycalculated in the respective positional relationships in the objects areaccumulated, so the matching can be performed at a high accuracy incomparison with the case where the matching is performed by applyingonly the similarity.

When the similarity Sim is larger than the first threshold th1, thematching unit judges that the two images are matched, so the matchingprocessing can be performed at high speed.

FIG. 18 is a functional block diagram of an image matching apparatus ofa fourth embodiment according to the present invention.

An image matching apparatus 1 c according to the present embodiment, inthe functional block diagram illustrated in a hardware, has theapproximately same configuration as the image matching apparatus 1according to the above embodiment, and, for example, has the image inputunit 11, the memory 12, the FFT processing unit 13, the coordinatetransform unit 14, the Hough transform unit 15, the CPU 16, and theoperation processing unit 17 as shown in FIG. 1.

The image matching apparatus 1 c, in the functional block diagramillustrated in a software, has the approximately same configuration asthe image matching apparatus according to the above describedembodiments, and the CPU 16 executes the program PRG to realizefunctions of the position correction unit 161, the Hough transform unit162, an extraction unit 163 c, and the judgment unit 164. In FIG. 18,components the same as those of the above embodiments are assigned thesame notations and explanations thereof are omitted. And only differentpoint will be described.

A large different point is the following: the extraction unit 163 cbeing different.

In the present embodiment, the extraction unit 163 c extracts regionswhere the degree of the overlap of the curved patterns in thetransformed image is equal to or greater than the threshold set inadvance, from the first transformed image and the second transformedimage, and determines the threshold based on a size of the extractedregion such that the size of the extracted region is larger than the setvalue.

The extraction unit 163 c determines the threshold based on the size ofthe extracted region such that the size of the extracted region iswithin the set range.

FIGS. 19A to 19F are views for explaining an operation of the imagematching apparatus 1 c shown in FIG. 17.

FIG. 19A is a view showing a specific example of an image IM11. FIG. 19Bis a view showing an image in which a region equal to or greater thanthe first threshold is extracted from the image IM11 shown in FIG. 19A.FIG. 19C is a view showing an image in which a region equal to orgreater than a second threshold larger than the first threshold isextracted from the image IM11 shown in FIG. 19A.

FIG. 19D is a view showing a specific example of an image IM12. FIG. 19Eis a view showing an image in which a region equal to or greater thanthe first threshold is extracted from the image IM12 shown in FIG. 19D.FIG. 19F is a view showing an image in which a region equal to orgreater than a second threshold larger than the first threshold isextracted from the image IM12 shown in FIG. 19D.

For example, in the above described embodiments, when performing theHough transform processing to the registered image and the image to bematched prior to extracting the amount of features (parameter) from thetransformed image, since the threshold is fixed, the sufficient amountof features may not be extracted or the redundant amount of the featuresmay be extracted depending on the images.

In detail, when performing the Hough transform processing to the imageIM11 shown in FIG. 19A before extracting the region larger than thefirst threshold from the transform image, an image IM111 shown in FIG.19B is generated, otherwise, when extracting the region larger than thesecond threshold, an image IM112 shown in FIG. 19C is generated.

When performing the Hough transform processing to the image IM12 shownin FIG. 19D prior to extracting the region larger than the firstthreshold from the transform image, an image IM121 shown in FIG. 19E isgenerated, otherwise, when extracting the region larger than the secondthreshold, an image IM122 shown in FIG. 19F is generated.

For example, the images IM112 and IM121 are specific examples includingthe amount of the data (features) appropriate to the matchingprocessing.

The image IM111 is a specific example including the amount of the data(features) redundant to the matching processing.

The image IM122 is a specific example extracted with the amount of data(features) insufficiently to the matching processing.

In the case of where the threshold is fixed, for example, the comparisonand matching processing is performed to the image IM111 and the imageIM121 or the image IM112 and the image IM122, so judgment accuracy maysometimes be insufficient.

For example, while changing the threshold, the comparison and matchingprocessing are performed to the image IM112 and the image IM121including the amount of data (features) sufficiently to the matchingprocessing, so the judgment accuracy is improved.

Therefore, the extraction unit 163 c according to the presentembodiment, in the case where the amount of features extracted byapplying a certain threshold is the out of the suitable range for thematching when extracting the amount of features to the transformed imageto which the Hough transformed processing was performed, changes thethreshold and extracts the amount of the features again, and determinesthe amount of feature so as to be within the suitable range.

FIG. 20 is a flow chart for explaining an operation of the imagematching apparatus according to the present embodiment. While referringto FIG. 20, the operation of the image matching apparatus, mainly adifferent point from the embodiment shown in FIG. 14, will be described.

The operations of steps ST1 to ST22 are the same and explanations willbe omitted.

At step ST231, the extraction unit 163 c extracts a parameter portionexceeding the threshold set in advance from the respective transformedimages S1612 and S1622 generated by the Hough transform unit 162, as theamount of features.

The extraction unit 163 c determines the threshold based on the size ofthe extracted region such that the size of the extracted region iswithin the set range.

In detail, the extraction unit 163 c determines the threshold such thatthe size of the extracted region is larger than the minimum value minbased on the size of the extracted region, also determines the same suchthat the size of the extracted region is smaller than the maximum valuemax larger than the minimum value min.

For example, at step ST232, the extraction unit 163 c judges whether ornot the size of the region extracted as the amount of features is largerthan the minimum value min, and reduces the threshold at a predeterminedamount if it judges it is not larger (ST233), the routine returns tostep ST321.

Otherwise, at step ST232, the extraction unit 163 c judges that the sizeof the region extracted as the amount of features is larger than theminimum value min, the routine proceeds to step ST234.

At step ST234, the extraction unit 163 c judges whether or not the sizeof the region extracted as the amount of the features is smaller thanthe maximum value max, and increases the threshold at the predeterminedamount if it judges it is not smaller (ST233), the routine returns tostep ST231.

Otherwise, at step ST234, the extraction unit 163 c judges that the sizeof the region extracted as the amount of the features is smaller thanthe maximum value max, the routine proceeds to step ST24.

The following steps of step ST24 are the same as the processing shown inFIG. 14 and explanations will be omitted.

As described above, the extraction unit 163 c determines the thresholdsuch that the size of the extracted region is within the set range basedon the size of the extracted region, so data redundant to the matchingprocessing can be reduced and data suitable for the matching processingcan be extracted.

The image appropriate for the matching processing can be obtained, sothe matching unit 164 can perform the matching processing at a highaccuracy.

In the case where an imaging system increasingly changes and the amountof data of the input image sharply changes, the comparison and matchingprocessing can be performed without any change.

FIG. 21 is a flow chart for explaining an operation of an image matchingapparatus of a fifth embodiment according to the present invention.

The image matching apparatus according to the present embodiment, in thefunctional block diagram illustrated in a hardware, has theapproximately same configuration as the image matching apparatusaccording to the above embodiments, and, for example, has the imageinput unit 11, the memory 12, the FFT processing unit 13, the coordinatetransform unit 14, the Hough transform unit 15, the CPU 16, and theoperation processing unit 17 as shown in FIG. 1.

For example, in the case where the amount of features of the registeredimage and the image to be matched is remarkably few, the amount of thefeatures insufficient to the comparison and matching processing may beobtained, so an insufficient judgment accuracy may be obtained.

The image matching apparatus according to the present embodimentextracts the region where the degree of the overlap of the curvedpatterns in the transformed image is equal to or greater than thethreshold set in advance, the image is deleted in the case where thesize of the extracted region is smaller than the set value.

While referring to FIG. 21, mainly a different point from the aboveembodiments in the case where a certain image data is stored as theregistered image for matching will be described, for example, in anapplication of a personal matching apparatus applied with a blood vesselpattern.

At step ST101, the CPU 16 causes the image input unit 11 to capture animage data to be stored as the registered image AIM, and stores theimage data in the memory 12. The CPU 16 causes the Hough transform unit15 to perform the Hough transform processing to the captured image data(ST102).

At step ST103, the CPU 16 extracts a region exceeding the threshold setin advance as the amount of the features (ST103).

At step ST104, the CPU 16 judges whether or not the size of theextracted region is larger than the minimum value min which is minimumnecessary for the matching processing, decides that the amount of theinformation sufficiently to the registered image AIM is included if itis judged to be larger, and stores the image data in the memory 12(ST105).

Otherwise, at step ST104, if it is judged not to be larger than theminimum value min, the input image is deleted (ST106), and a re-input ofthe image data is requested, for example, by displaying a notice thatthe amount of the information as the registered image AIM isinsufficiency (ST107). Then, the above described matching processing isperformed.

In the present embodiment as described above, in registering an image,the region where the degree of the overlap of the curved patterns in thetransformed image to be registered is equal to or greater than thethreshold set in advance is extracted, then the image is deleted in thecase where the size of the extracted region is less than the set value(the minimum value min), so the image to be registered having relativelylarge amount of the features concerning the matching apparatus can beobtained steadily. And the matching processing can be performed at ahigh accuracy.

FIG. 22 is a flow chart for explaining an operation of an image matchingapparatus of a sixth embodiment according to the present embodiment.

The case where the image to be matched RIM input from an external ismatched with the registered image AIM stored in the memory 12 inadvance, will be described with reference to FIG. 22.

At step ST201, the CPU 16 causes the image input unit 11 to capture theimage data as the image to be matched RIM, and stores the image data inthe memory 12. The CPU 16 causes the Hough transform unit 15 to performthe Hough transform processing to the captured image to be matched(ST202).

At step ST203, the CPU 16 extracts the region exceeding the thresholdset in advance as the amount of the features (ST203).

At step ST204, the CPU 16 judges whether or not the size of theextracted region is larger than the minimum value min which is minimumnecessary for the matching processing, decides that the amount of theinformation is no sufficient to the image to be matched RIM if it isjudged not to be larger, deletes the image (ST205), requests there-input of the image data, for example, by displaying a notice that theamount of the information is not sufficient (ST206), and terminates thematching processing.

Otherwise, in the judgment at step ST204, if it is judged to be largerthan the minimum value min, the CPU 16 judges that the amount ofinformation is sufficient to the image to be matched RIM, the routineproceeds to the following matching processing of steps ST2 to ST27.

The following operations of steps ST2 to ST27 are the same as theoperation shown in FIG. 14 and explanations will be omitted.

As described above, in the present embodiment, in matching images, theregion where the degree of the overlap of the curved patterns in thetransformed image to be matched is equal to or greater than thethreshold set in advance, is extracted, and the image is deleted in thecase where the size of the extracted region is less than the set value(the minimum value min), so the image to be matched including relativelylarge amount of the features applied to the matching apparatus can beobtained steadily. And the matching processing can be performed at ahigh accuracy.

Note that, the present invention is not limited to the presentembodiments and it can be modified in various ways.

For example, in the present embodiment, though the similarity generationunit calculates the similarity by applying the formula (2), it is notlimited thereto. For example, the similarity generation unit may performa processing in which the similarity suitable to the correlation of thelinear components (linear shaped patterns) is calculated.

In the present embodiment, though the Fourier transform processing, thelogarithmic transform processing, and a logarithm-polar coordinatestransform processing are performed and the amount of parallel movementon the Fourier and Mellin space is calculated to thereby generate themagnification information and the rotation angle information, it is notlimited thereto. For example, the coordinates transform processing maybe performed on a space detectable to the magnification information andthe rotation angle information.

Though the first threshold th1 and the second threshold th2 are fixedvalue, it is not limited thereto. For example, the respective thresholdsare variable depending on an image pattern, so the matching processingcan be performed at a higher accuracy.

1. An image matching method for performing a matching images to linearcomponents in a first image and a second image, the method comprising: afirst step of performing an image processing for performing points ineach image of the first image and the second image to a curved patternand the linear components in each image to a plurality of overlappedcurved-patterns, based on a distance from a reference position to ashortest point in a straight line passing through a point in the imageand an angle between a straight line passing though the referenceposition and the shortest point and a reference axis including thereference position, and generating a first transformed image and asecond transformed image, and a second step of performing a matching ofthe first image and the second image based on a degree of an overlap ofthe patterns in the first transformed image and the second transformedimage generated in the first step and a matching or mismatching of thepatterns in the first and second transformed images.
 2. An imagematching method as set forth in claim 1, wherein the first stepcomprises a third step of extracting regions each of which indicates adegree of the overlap of the curved patterns in the transformed imageequal to or greater than a threshold set in advance, from the firsttransformed image and the second transformed image, and wherein, in thesecond step, the matching of the first image and the second image arecarried out based on the matching or mismatching of the patterns in theregions extracted from the first transformed image and the secondtransformed image respectively in the third step.
 3. An image matchingmethod as set forth in claim 2, wherein, in the third step, thethreshold is determined based on a size of the extracted region suchthat the size of the extracted region is larger than the set value. 4.An image matching method as set forth in claim 2, wherein, in the thirdstep, the threshold is determined based on the size of the extractedregion such that the size of the extracted region is within the setvalue.
 5. An image matching method as set forth in claim 2, wherein, inthe third step, the image is deleted when the size of the extractedregion is less than the set value.
 6. An image matching method as setforth in claim 1, wherein, in the first step, a Hough transformprocessing is performed to the first image and the second image togenerate the first transformed image and the second transformed image.7. An image matching method as set forth in claim 1, wherein, in thesecond step, a comparison processing is performed to a plurality ofdifferent positional relationships in the first transformed image andthe second transformed image generated in the first step, a similarityas a correlation value is generated based on a result of the comparisonprocessing, and the matching of the first image and the second image arecarried out based on the generated similarity.
 8. An image matchingmethod as set forth in claim 1, before the first step, furthercomprising a tenth step of performing a position correction processingto the first image and the second image, wherein, in the first step, theimage processing is performed to the first image and the second imagewhich are results of the position correction processing in the tenthstep to generate the first transformed image and the second transformedimage.
 9. An image matching method as set forth in claim 8, wherein, inthe tenth step, as the position correction processing, a correlationvalue is generated based on a phase component which is a result of arotation angle correction processing or an enlargement ratio correctionprocessing and the Fourier transform processing to the first image andthe second image, and the position correction processing is performed tothe first image and the second image based on the generated correlationvalue.
 10. An image matching method as set forth in claim 8, wherein, inthe tenth step, a plurality of the correlation value indicating acorrected position is generated by a correlation processing to the firstimage and the second image, and a plurality of the position correctionprocessing is performed to the first image and the second image based onthe generated correlation value, in the first step, the image processingis performed to the results of the plurality of the position correctionprocessing of the first image and the second image in the tenth step togenerate the first transformed image and the second transformed image,and in the second step, the correlation value is generated based on thepatterns in the first transformed image and the second transformed imagegenerated in the first step, and the matching of the first image and thesecond image are carried out based on the generated correlation valueand the threshold set in advance.
 11. An image matching method as setforth in claim 10, wherein, in the second step, the matching of thefirst image and the second image are carried out to the result of theplurality of the position correction processing generated in the firststep based on the total amount of the correlation value corresponding todifferent positions and the threshold set in advance.
 12. An imagematching method for performing a matching images to linear components ina first image and a second image, the method comprising: a first step ofperforming a Hough transform processing to the first image and thesecond image to generate a first transform image and a second transformimage, and a second step of performing a matching of the first image andthe second image based on a degree of an overlap of patterns in thefirst transformed image and the second transformed image generated inthe first step and a matching or mismatching of the patterns in thesame.
 13. An image matching apparatus performing a matching to linearcomponents in a first image and a second image, the apparatuscomprising: a transform means for performing an image processing to thefirst image and the second image, the image processing by which pointsin each image are transformed to a curved pattern and linear componentsin each image are transformed to a plurality of overlappedcurved-patterns based on a distance from a reference position to ashortest point in a straight line passing through a point in the imageand an angle between a straight line passing though the referenceposition and the shortest point and a reference axis including thereference position, and generating a first transformed image and asecond transformed image, and a matching means for performing a matchingof the first image and the second image based on a degree of an overlapof the patterns in the first transformed image and the secondtransformed image generated by the transform means and a matching ormismatching of the patterns in the first and second transformed images.14. An image matching apparatus as set forth in claim 13, furthercomprising a extraction means for extracting a region where the degreeof the overlap of the curved patterns in the transformed image is equalto or greater than a threshold set in advance, from the firsttransformed image and the second transformed image, wherein the matchingmeans performs the matching of the first image and the second imagebased on the matching or mismatching of the patterns in the regionsextracted by the extraction means from the first transformed image andthe second transformed image respectively.
 15. An image matchingapparatus as set forth in claim 14, wherein the extraction meansdetermines the threshold based on a size of the extracted region so asto be larger than the set value.
 16. An image matching apparatus as setforth in claim 14, wherein the extraction means determines the thresholdbased on the size of the extracted region so as to be within the setvalue.
 17. An image matching apparatus as set forth in claim 14, whereinthe extraction means deletes the image when the size of the extractedregion is equal to or less than the set value.
 18. An image matchingapparatus as set forth in claim 13, wherein the transform means performsa Hough transform processing to the first image and the second image togenerate the first transformed image and the second transformed image.19. An image matching apparatus as set forth in claim 13, wherein thematching means performs a comparison processing to a plurality ofdifferent positional relationships in the first transformed image andthe second transformed image generated by the transform means, generatesa similarity as a correlation value based on a result of the comparisonprocessing, and performs the matching of the first image and the secondimage based on the generated similarity.
 20. An image matching apparatusas set forth in claim 13, further comprising a position correction meansfor performing a position correction processing to the first image andthe second image, before an operation of the transform means, whereinthe transform means performs the image processing to results of theposition correction processing of the first image and the second imageperformed by the position correction means to generate the firsttransformed image and the second transformed image.
 21. An imagematching apparatus as set forth in claim 20, wherein the positioncorrection means generates a correlation value based on a phasecomponent which is a result of a rotation angle correction processing oran enlargement ratio correction processing and the Fourier transformprocessing to the first image and the second image, and performs theposition correction processing to the first image and the second imagebased on the generated correlation value.
 22. An image matchingapparatus as set forth in claim 20, wherein the position correctionmeans generates a plurality of the correlation values each indicating acorrected position by a correlation processing based on the first imageand the second image, and performs a plurality of the positioncorrection processing to the first image and the second image based onthe generated correlation value, the transform means performs the imageprocessing to the results of the plurality of the position correctionprocessing of the first image and the second image by the positioncorrection means to generate the first transformed image and the secondtransformed image, and the matching means generates the correlationvalue based on the patterns in the first transformed image and thesecond transformed image generated by the transform means, and performsthe matching of the first image and the second image based on thegenerated correlation value and the threshold set in advance.
 23. Animage matching apparatus as set forth in claim 22, wherein the matchingmeans performs the matching of the first image and the second image tothe result of the plurality of the position correction processinggenerated by the transform means based on the total amount of thecorrelation value corresponding to different positions and the thresholdset in advance.
 24. An image matching apparatus performing a matching tolinear components in a first image and a second image, the apparatuscomprising: a transform means for performing a Hough transformprocessing to the first image and the second image to generate a firsttransform image and a second transform image, and a matching means forperforming a matching of the first image and the second image based on adegree of an overlap of patterns in the first transformed image and thesecond transformed image generated by the transform means and a matchingor mismatching of the patterns in the first and second transformedimages.
 25. A program that causes an information processing device toperform a matching images to linear components in a first image and asecond image, the program comprising: a first routine for performing animage processing to the first image and the second image, by whichpoints in each image are transformed to a curved pattern and linearcomponents in each image are transformed to a plurality of overlappedcurved-patterns based on a distance from a reference position to ashortest point in a straight line passing through a point in the imageand an angle between a straight line passing though the referenceposition and the shortest point and a reference axis including thereference position, and generating a first transformed image and asecond transformed image, and a second routine for performing a matchingof the first image and the second image based on a degree of an overlapof the patterns in the first transformed image and the secondtransformed image generated in the first routine and a matching ormismatching of the patterns in the first and second transformed images.26. A program as set forth in claim 25, in the first routine, furthercomprising a third routine for extracting regions each of whichindicates a degree of the overlap of the curved patterns in thetransformed image equal to or greater than a threshold set in advance,from the first transformed image and the second transformed image,wherein, in the second routine, the matching of the first image and thesecond image are carried out based on the matching or mismatching of thepatterns in the regions extracted from the first transformed image andthe second transformed image respectively in the third routine.
 27. Aprogram as set forth in claim 26, wherein, in the third routine, thethreshold is determined based on a size of the extracted region suchthat the size of the extracted region is larger than the set value. 28.A program as set forth in claim 26, wherein, in the third routine, thethreshold is determined based on the size of the extracted region suchthat the size of the extracted region is within the set value.
 29. Aprogram as set forth in claim 26, wherein, in the third routine, theimage is deleted when the size of the extracted region is equal to orless than the set value.
 30. A program as set forth in claim 25,wherein, in the first routine, a Hough transform processing is performedto the first image and the second image to generate the firsttransformed image and the second transformed image.
 31. A program as setforth in claim 25, wherein, in the second routine, a comparisonprocessing is performed to a plurality of different positionalrelationships in the first transformed image and the second transformedimage generated in the first routine, a similarity as a correlationvalue is generated based on a result of the comparison processing, andthe matching of the first image and the second image are carried outbased on the generated similarity.
 32. A program as set forth in claim25, before the first routine, further comprising a tenth routine forperforming a position correction processing to the first image and thesecond image, wherein, in the first routine, the image processing isperformed to results of the position correction processing to the firstimage and the second image in the tenth routine to generate the firsttransformed image and the second transformed image.
 33. A program as setforth in claim 32, wherein, in the tenth routine, as the positioncorrection processing, a correlation value is generated based on a phasecomponent which is a result of a rotation angle correction processing oran enlargement ratio correction processing and the Fourier transformprocessing to the first image and the second image, and the positioncorrection processing is performed to the first image and the secondimage based on the generated correlation value.
 34. A program as setforth in claim 32, wherein, in the tenth routine, a plurality of thecorrelation values each indicating a corrected position is generated bya correlation processing based on the first image and the second image,and a plurality of the position correction processing are performed tothe first image and the second image based on the generated correlationvalue, in the first routine, the image processing is performed to theresults of the plurality of the position correction processing of thefirst image and the second image in the tenth routine to generate thefirst transformed image and the second transformed image, and in thesecond routine, the correlation value is generated based on the patternsin the first transformed image and the second transformed imagegenerated in the first routine, and the matching of the first image andthe second image are carried out based on the generated correlationvalue and the threshold set in advance.
 35. A program as set forth inclaim 34, wherein, in the second routine, the matching of the firstimage and the second image are carried out to the result of theplurality of the position correction processing generated in the firstroutine based on the total amount of the correlation value correspondingto different positions and the threshold set in advance.
 36. A programthat causes an information processing device to perform a matching tolinear components in a first image and a second image, the programcomprising: a first routine for performing a Hough transform processingto the first image and the second image to generate a first transformimage and a second transform image, and a second routine for performinga matching of the first image and the second image based on a degree ofan overlap of patterns in the first transformed image and the secondtransformed image generated in the first routine and a matching ormismatching of the patterns in the first and second transformed images.